Multi-Armed Bandit

The multi-armed bandit problem is a fundamental reinforcement learning framework modeling the exploration-exploitation dilemma, where an agent repeatedly chooses between multiple options (arms) with unknown reward distributions, aiming to maximize cumulative rewards over time. Each arm pull yields stochastic feedback, forcing the agent to balance exploiting known good options against exploring potentially better alternatives. This classical problem captures decision-making under uncertainty and has applications wherever resource allocation must be optimized without complete information. Example: DEX liquidity pool allocation uses multi-armed bandit solutions where traders must decide which pools to provide liquidity to, trading off between proven high-fee pools (exploitation) and emerging pools with potentially better future returns (exploration). Why it matters for AI and data in Web3: Multi-armed bandit algorithms optimize capital allocation and risk management in DeFi protocols, helping users and protocols allocate resources efficiently across competing opportunities like liquidity pools, yield strategies, and market-making opportunities without full information.

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